Our Research

One hundred years on, Einstein's General Theory of Relativity (GR) remains the most remarkable incubator for new ideas in gravitational physics. From cosmology to condensed matter analogues, it has shaped the way we think about our universe across a multitude of scales in a rather fundamental way. But how well do we really understand gravity? Group members working in this area use cutting edge techniques ranging from scattering amplitudes and cosmology, to the gauge theory/gravity duality to study the structure and dynamics of gravity in 2,3 and 4-dimensional spacetime.

Quantum matter, states of matter that manifest quantum phenomena macroscopically, have catalysed a revolution in low energy physics. From the quantum Hall effect, to graphene and its myriad applications, to qubit candidates for fault-tolerant quantum computing, these novel phases of matter bring together research at the cutting edge of high energy physics, condensed matter and mathematics. A key driver of progress in this area is the recent discovery of a web of 3D dualities that unveil intricate relationships between various gauge theories in 3-spacetime dimensions. QGASlab members were at the forefront of this discovery and continue to make advances in the area of low-energy non-supersymmetric dualities. In addition, project members are currently working on trying to understand the behaviour of electronic matter in extreme magnetic fields; the effects of geometry in the quantum Hall effect, the physics of exciting new materials such as twisted bi-layer graphene and quantum batteries and the possibility of observing ultra-quantum matter in extreme astrophysical environments such as the compressed atmosphere of a neutron star.

Theoretical physicists are famous for solving "spherical cow" problems - idealised systems that are clean, ordered and, for the most part, tractable. Nature, on the other hand, is overwhelmingly disordered and generically chaotic - in the sense of exhibiting hypersensitive dependence on initial conditions. These effects cooperate to produce some of the richest, and most difficult to understand, physiccal systems that range from excited atomic systems through to quantum computers. Project members in this area work on, among others, the SYK model of disordered quantum mechanics and its higher-dimensional generalizations such as the Murugan-Stanford-Witten (MSW) models of disordered CFTs; the onset of chaos in quantum many-body systems; information scrambling in quantum networks and novel probes of quantum chaos.

What is information, at quantum scales? How is it processed? How is it stored in quantum systems? How is it transmitted between such systems? What is the role of entanglement in such processes? These are some of the questions that drive the field of quantum information theory that lies at the core of the emerging science of Quantum Computing. Project members working in this area are interested in understanding the geometry of information space with probes such as the Fisher Information metric; computational complexity and similar information theoretic measures in the context of quantum field theory and quantum gravity and applications of tools from algebraic topology, such as persistent homology and discrete Morse theory, to data analysis.

The recent progress in machine learning and artificial intellegence, through a combination of inceased computational power and algorithm development, has brought about some of the most significant developments in science in decades. From Google Deepmind’s recently reported ‘solution’ of the 50-year old problem of protein folding, to the application of graph neural networks to vastly improve signal detection in the IceCube neutrino detection lab- oratory; it is clear the machine learning is here to stay, with an impact on physics research in the 21st century to rival that of symbolic computing in the 20th. Group members working in this area are, among others, using AI to explore topological quantum materials; constructing neural networks to probe the onset of thermalization in quantum many-body system; using machine learning to extract physics from big data generated by a number of astrophysics observations such as HIRAX and using reinforcement learning to explore a number of real-world problems.